I’ve had very good success in similar setups where you have some sort of “oracle” and can generate enormous corpuses of test data, such that you really, really trust the LLM code must work for the inputs you expect it’ll ever need to handle.

Makes me think of all the algorithms we specify in proof languages and then hand-implement in production languages - this setup could maybe let you just specify the proof of an algorithm and then let LLMs derive efficient implementations with the (slow) proof as an oracle